Benchmark Problem "Omnidirectional vision SLAM - Bicocca_2009-02-25b"

Creator: GF
Labels: first batch, artificial light, static, indoor, SLAM

Robot path for the Bicocca_2009-02-25b dataset Perform a map building activity with SLAM (online), using the sensor data files provided below. Such files come from the Bicocca_2009-02-25b data-collection session; they include output and calibration information associated to omnidirectional color camera, IMU, odometry, and the ground truth for the specified session.
Please note that the Bicocca_2009-02-25b session also includes other data files, not used for this Benchmark Problem.

This BP belongs to the very first batch of problems published by Rawseeds; other BPs will follow.

Attached Files

Evaluation Methodologies

The provided solutions will be evaluated and scored with respect to the following evaluation methodologies:

  • ATE compares the trajectory of a robot, as reconstructed by an algorithm using real sensor data as its input, to the actual trajectory (ground truth). ATE is a mandatory performance measure. Please ... - Absolute Trajectory Error
  • ME compares the map of an environment, as reconstructed by an algorithm using real sensor data as its input, to the actual map of the location (ground truth). ME is a recommended performance measure. - Mapping Error
  • RPE measures the accuracy of a SLAM result, as reconstructed by an algorithm using real sensor data as its input, by comparing the reconstructed relative transformations between nearby poses to the ac... - Relative Pose Error
  • REC provides a basic estimate of how the running time of an algorithm (which uses real sensor data as its input) scales as the quantity of data available to be processed increases. REC is a mandatory... - Rough Estimate of Complexity
  • SLE aims to evaluate the overall quality of a SLAM algorithm by actually using its output in a realistic application. The SLAM algorithm, fed with real sensor data from a robot, is used to build a map... - Self Localization Error